Outline:¶
- What is the AMI?
- What is Sunyaev Zeldovich?
- What did this paper find?
- Open questions?
What is the Sunyaev-Zel'dovich effect?¶
"when CMB photons inverse-Compton scattered by hot plasma in a galaxy cluster's potential well"
-Shimwell et al. (2013)
- Galaxy clusters can be traced by CMB fluctuations!
- SZ causes a drop in flux at freq. < 217 GHz
What is AMI?¶
- Arcminute Microkelvin Imager:
"blind galaxy cluster survey [...] aiming to detect via the Sunyaev-Zel'dovich effect"
-page 1 - New digital correlator
- Large-array: Sensitivity and resolution - foreground/background isolation
- Small array: High surface brightness sensitivity - SZ imaging
#### Small Array:
#### Large array
- 1972: Sunyaev & Zel'dovich claim CMB deviations can identify galaxy clusters
- 2008-2011: AMI observes "blindly" at 15.7 GHz - detects several clusters
- 2013: Schiwell+ report detection of "extended and unusual cluster"
J0300+2613via SZ - 2011-present: Anomalous microwave emission/spinning dust annoys cosmologists
- 2015: AKARI Far IR data relased
- 2017: Perrott+ claim
J0300+2613is just galactic dust! (not SZ, but AME)
What was their strategy?¶
- Repeat observation with new correlator
- Compare with newly available IR and MW data (i.e. AKARI!)
- Rule-out synchrotron with
GLEAM200 MHz data - Rule-out free-free with
GB65 GHz data - Rule-out thermal dust emission via blackbody fit
Rule out synchrotron with GLEAM 200 MHz:¶
Rule out free-free with GB6 5 GHz:¶
What did this paper find?¶
J0300+2613correlates with IR emission- Obsvervation with upgraded instrument shows much weaker SZ-effect
- Unlikely to be synchrotron or free-free emission
Open Questions¶
- What does
J0300+2613look like in MIR data? - Could other SZ "clusters" be mislabled?
- Even if
J0300+2613is really Galactic emission-Is it necessarily AME?- What about free-free?
- Synchrotron?
- Thermal dust?
What does J0300+2613 look like in MIR data?¶
- We can find out:
In [8]:
#Load `J0300+2613` coordinates into python:
from astropy.coordinates import SkyCoord # Low-level frames
import astropy.units as u
coord = SkyCoord('3:00:08.9','+26:16:29.1',frame='icrs', unit=(u.hourangle, u.deg))
#Convert to degrees RA DEC:
coord
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In [10]:
#So the kernel doesn't crash when we plot stuff in a notebook
%matplotlib inline
# Convenient package for plotting FITS images
import aplpy
def fits_display(image_file):
# Create a new figure
fig = aplpy.FITSFigure(image_file)
# Show the colorscale
fig.show_colorscale()
# Make ticks white
fig.ticks.set_color('white')
# Make labels smaller
fig.tick_labels.set_font(size=15)
# Round out those tick labels:
fig.tick_labels.set_xformat('ddd.dd')
fig.tick_labels.set_yformat('ddd.dd')
# Overlay a grid
fig.add_grid()
fig.grid.set_alpha(0.9)
# Add a colorbar
fig.add_colorbar()
fig.colorbar.set_axis_label_text('Intensity (MJy/sr)')
return fig
How the region looks if we make an exact cutout from raw IRC data:¶
Zodi is not subtracted!
In [18]:
# 9 micron data
fits_display('/home/aaronb/J003+2613/image/J003+2613_S.fits')
# 18 micron data
fits_display('/home/aaronb/J003+2613/image/J003+2613_L.fits')
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In [19]:
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In [17]:
# How the region looks in Zodi-subtracted All-sky Tiles
In [15]:
# 9 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G156m27/G156m27_S_intensity.fits')
# 18 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G156m27/G156m27_L_intensity.fits')
Out[15]:
In [14]:
# 9 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G157m30/G157m30_S_intensity.fits')
# 18 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G157m30/G157m30_L_intensity.fits')
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